Video summarization technology extracts key frames or video clips that can most effectively express video content from the original long video, so that the summarized video clips contain the content information that users are most interested in, which is convenient for users to quickly browse and retrieve the video. In this paper, we regard video summarization as a sequence annotation problem. Different from the existing methods using recurrent models, this paper proposes a fully convolutional neural network model combining spatial attention mechanism to solve the video summarization problem. Firstly, the pre-training model is used to extract the cube features of the input video frame, then the cube features are aggregated into the attention vector by the attention mechanism, which is input into the fully convolutional neural network model for binary classification, and then the video summary is generated. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of the proposed method.
KEYWORDS: Control systems, Information security, Databases, Network security, Mining, Telecommunications, Control systems design, Safety, Computer security, Analytical research
Through in-depth study of the current vulnerability discovery technology, this paper analyzes the advantages and disadvantages of common vulnerability discovery technology, as well as the application field. Based on the CVE vulnerability library, the existing OpenVAS source code design was improved, and its scanning process was improved to enhance the ability of scanning known vulnerabilities. For the unknown vulnerability mining, optimize the design of the vulnerability mining test generating algorithm and vulnerability mining algorithm, carry out network vulnerability mining on the target industrial control test system, so as to obtain the known or unknown vulnerability analysis report of the industrial control system, and form the security assessment report and security response strategy of the industrial control system.
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